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Wrangle weight-for-height data

Source: R/wrangle_wfhz.R
mw_wrangle_wfhz.Rd

Calculate z-scores for weight-for-height (WFHZ) and identify outliers based on the SMART methodology.

Usage

mw_wrangle_wfhz(df, sex, weight, height, .recode_sex = TRUE, .decimals = 3)

Arguments

df

A dataset object of class data.frame to wrangle data from.

sex

A numeric or character vector of child's sex. Code values should only be 1 or "m" for males and 2 or "f" for females. Make sure sex values are coded in either of the aforementioned before to call the function. If input codes are neither of the above, the function will stop execution and return an error message with the type of mismatch.

weight

A vector of class double of child's weight in kilograms. If the input is of a different class, the function will stop execution and return an error message indicating the type of mismatch.

height

A vector of class double of child's height in centimeters. If the input is of a different class, the function will stop execution and return an error message indicating the type of mismatch.

.recode_sex

Logical. Set to TRUE if the values for sex are not coded as 1 (for males) or 2 (for females). Otherwise, set to FALSE (default).

.decimals

The number of decimals places the z-scores should have. Default is 3.

Value

A data frame based on df. New variables named wfhz and flag_wfhz, of child's WFHZ and detected outliers, will be created.

References

SMART Initiative (2017). Standardized Monitoring and Assessment for Relief and Transition. Manual 2.0. Available at: https://smartmethodology.org.

See also

flag_outliers() remove_flags()

Examples

mw_wrangle_wfhz(
  df = anthro.01,
  sex = sex,
  weight = weight,
  height = height,
  .recode_sex = TRUE,
  .decimals = 2
)
#> ================================================================================
#> # A tibble: 1,191 × 13
#>    area      dos        cluster  team   sex dob      age weight height edema
#>    <chr>     <date>       <int> <int> <dbl> <date> <int>  <dbl>  <dbl> <chr>
#>  1 District… 2023-12-04       1     3     1 NA        59   15.6  109.  n    
#>  2 District… 2023-12-04       1     3     1 NA         8    7.5   68.6 n    
#>  3 District… 2023-12-04       1     3     1 NA        19    9.7   79.5 n    
#>  4 District… 2023-12-04       1     3     2 NA        49   14.3  100.  n    
#>  5 District… 2023-12-04       1     3     2 NA        32   12.4   92.1 n    
#>  6 District… 2023-12-04       1     3     2 NA        17    9.3   77.8 n    
#>  7 District… 2023-12-04       1     3     2 NA        20   10.1   80.4 n    
#>  8 District… 2023-12-04       1     3     2 NA        27   11.7   87.1 n    
#>  9 District… 2023-12-04       1     3     1 NA        46   13.6   98   n    
#> 10 District… 2023-12-04       1     3     1 NA        58   17.2  109.  n    
#> # ℹ 1,181 more rows
#> # ℹ 3 more variables: muac <int>, wfhz <dbl>, flag_wfhz <dbl>